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Detecting change in processes using comparative trace clustering

Hompes, B.F.A. ; Buijs, J.C.A.M. ; van der Aalst, W.M.P. ; Dixit, P.M. ; Buurman, J. ; Caravolo, P. ; Rinderle-Ma, S.; Information Systems WSK&I

Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015, 2015, Issue SIMPDA 2015, pp.95-108

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  • Nhan đề:
    Detecting change in processes using comparative trace clustering
  • Tác giả: Hompes, B.F.A. ; Buijs, J.C.A.M. ; van der Aalst, W.M.P. ; Dixit, P.M. ; Buurman, J. ; Caravolo, P. ; Rinderle-Ma, S.
  • Information Systems WSK&I
  • Chủ đề: Concept Drift ; Process Comparison ; Process Mining ; Trace Clustering ; Computer Science(All)
  • Là 1 phần của: Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015, 2015, Issue SIMPDA 2015, pp.95-108
  • Mô tả: Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights.
  • Ngôn ngữ: English

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